Book ChapterDOI
On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters
Michalis Smyrnakis,Hongyang Qu,Dario Bauso,Sandor M. Veres +3 more
- pp 73-105
TLDR
In this paper, a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players' strategies, which can be used as a coordination mechanism between players when they should take decisions under uncertainty.Abstract:
This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.read more
References
More filters
Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Journal ArticleDOI
Equilibrium points in n-person games
TL;DR: A concept of an n -person game in which each player has a finite set of pure strategies and in which a definite set of payments to the n players corresponds to each n -tuple ofpure strategies, one strategy being taken for each player.
Book
The theory of learning in games
Drew Fudenberg,David K. Levine +1 more
TL;DR: Fudenberg and Levine as discussed by the authors developed an alternative explanation that equilibrium arises as the long-run outcome of a process in which less than fully rational players grope for optimality over time.
Book ChapterDOI
Markov games as a framework for multi-agent reinforcement learning
TL;DR: A Q-learning-like algorithm for finding optimal policies and its application to a simple two-player game in which the optimal policy is probabilistic is demonstrated.
Journal ArticleDOI
Stochastic Games
TL;DR: In a stochastic game the play proceeds by steps from position to position, according to transition probabilities controlled jointly by the two players, and the expected total gain or loss is bounded by M, which depends on N 2 + N matrices.